Assessment of Global Cloud Datasets from Satellites
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ASSESSMENT OF GLOBAL CLOUD DATASETS FROM SATELLITES:
Project and Database initiated by the GEWEX Radiation Panel
C .J. Stubenrauch1, W. B. Rossow2, S. Kinne3, S. Ackerman4, G. Cesana1, H. Chepfer1, B.
Getzewich11, L. Di Girolamo5, A. Guignard1, A. Heidinger6, B. Maddux4, P. Menzel6, P.
Minnis7, C. Pearl2, S. Platnick8, C. Poulsen9, J. Riedi10, S. Sun-Mack11, A. Walther4, D.
Winker7, S. Zeng10, G. Zhao5
1 Laboratoire de Météorologie Dynamique / IPSL / CNRS, Ecole Polytechnique, France
2 CREST Institute at City College of New York, USA
3 Max Planck Institute for Meteorology, Hamburg, Germany
4 CIMMS, University of Wisconsin, Madison, WI, USA
5 Department of Atmospheric Sciences, University of Illinois, Urbana, IL, USA
6 NOAA/NESDIS/STAR, Madison, WI, USA
7 NASA Langley Research Center, Hampton, VA, USA
8 NASA Goddard Space Flight Center, Greenbelt, USA
9 Rutherford Appleton Laboratory, Chilton, UK
10 Laboratoire d'Optique Atmosphérique / CNRS, Lille, France
11 Science Systems and Applications, Inc., Hampton, VA, USA
Corresponding author:
Dr Claudia Stubenrauch
Laboratoire de Météorologie Dynamique
Ecole Polytechnique
F-91128 Palaiseau cedex
France
Phone: +33 169335196 [email protected]
1 ABSTRACT
Clouds cover about 70% of the Earth's surface and play a dominant role in the energy and water cycle of our planet. Only satellite observations provide a continuous survey of the state of the atmosphere over the whole globe over the wide range of spatial and temporal scales that comprise weather and climate variability. Satellite cloud data records now exceed more than
25 years in length. However, climatologies compiled from different satellite datasets exhibit systematic differences and there have been questions as to the accuracy and limitations of the various sensors. The Global Energy and Water cycle Experiment (GEWEX) Cloud
Assessment, initiated in 2005 by the GEWEX Radiation Panel, provided the first coordinated intercomparison of publically available, standard global cloud products (gridded, monthly statistics) retrieved from measurements of multi-spectral imagers (also including multi-angle view and polarization capabilities), IR sounders and active lidar. Cloud properties under study include cloud amount, cloud height (in terms of pressure, temperature or altitude), cloud radiative properties (optical depth or emissivity), cloud thermodynamic phase and bulk microphysical properties (effective particle size and water path). Differences in average cloud properties, especially in the amount of high-level clouds, are mostly explained by instrument performance that determines their ability to detect and/or identify optically thin cirrus, especially when overlying low-level clouds. The study of long-term variations with these datasets requires consideration of many factors. A monthly, gridded database, in common format, facilitates further assessments, climate studies and the evaluation of climate models.
2 Capsule :
Cloud properties derived from space observations are immensely valuable for climate studies or model evaluation; this assessment has revealed how their statistics may be affected by instrument choices or retrieval methods but also highlight those well determined.
3 INTRODUCTION
The GEWEX Radiation Panel (GRP, now the GEWEX Data and Assessment Panel) initiated the GEWEX Cloud Assessment in 2005 to compare available, global, long-term cloud data products with International Satellite Cloud Climatology Project (ISCCP, Rossow and Schiffer 1999), which is the GEWEX cloud product and has been available since the
1980’s. The ISCCP cloud products were designed to characterize essential cloud properties and their variation on all key time scales to elucidate cloud dynamical processes and cloud radiative effects. The focus of the assessment is on the comparison of global climatological averages as well as their regional, seasonal and inter-annual variations derived from Level-3
(L3) cloud products (gridded monthly statistics). The presentations and discussions during four international workshops led to the current GEWEX Cloud Assessment database, including monthly averages, a measure of synoptic variability as well as histograms at a spatial resolution of 1° latitude x 1° longitude. It was created in a common netCDF format by the participating teams and is available at the GEWEX Cloud Assessment website: http://climserv.ipsl.polytechnique.fr/gewexca/, together with a detailed report (Stubenrauch et al. 2012).
The following article presents a summary of average cloud properties and their variability, as observed from space. The GEWEX Cloud Assessment database includes cloud properties retrieved from different satellite sensor measurements, undertaken at various local times and over various time periods (Tables 1 and 2). Table 3 summarizes the main characteristics of the cloud property retrievals (including spectral domain, spatial resolution, retrieval method as well as ancillary data used) leading to the twelve datasets that participated in the GEWEX
Cloud Assessment (Table 1).
4 SATELLITE REMOTE SENSING OF CLOUD PROPERTIES
Only satellite observations are capable of providing a continuous synoptic survey of the state of the atmosphere over the whole globe. Operational weather satellite sensors supply time records extending for at least 30 years. Whereas polar-orbiting, cross-track scanning sensors generally only provide global coverage at a particular local time of the day, geostationary satellites are placed at particular longitudes along the equator and permit higher frequency temporal sampling (15 minute to 3 hour intervals).
The relevant satellite sensors measure radiation scattered or emitted by the Earth’s surface and by the Earth’s atmosphere including clouds. To maximize the sensitivity to the presence of clouds and to determine key cloud properties, specific spectral domains are selected. The conversion of the measured radiances into cloud properties requires in general two steps:
cloud detection (or scene identification)
cloud property retrieval, based on radiative transfer and employing ancillary data to isolate
the cloud from surface and non-cloud atmospheric contributions
Clouds generally appear brighter and colder than the Earth’s surface. Cloudy scenes also generally exhibit larger spatial and temporal variability than cloud-free or so called clear sky scenes. However, difficulties in detecting clouds may arise when the radiance contrast is small
(e.g. clouds over already highly solar reflecting surfaces such as snow or ice, clouds with small thermal contrast to the surface below as for low-level clouds in humid boundary layers over ocean, or cloud edges) or when clear-sky scene variability is larger than usual (e.g. optically thin clouds over land areas or clouds over winter land areas).
Sensor types for retrieving cloud properties
Multi-spectral imagers are radiometers measuring at only a few discrete wavelengths, usually from the solar to thermal infrared spectrum. Nadir viewing with cross-track scanning capabilities, they have a spatial resolution from about 0.5 to 7 km (at nadir) and are the only
5 sensors aboard geostationary weather satellites as well as aboard polar orbiting satellites.
ISCCP uses a combination of these sensors from both, geostationary and polar orbiting satellites to resolve the diurnal cycle of clouds. The only commonly available wavelengths are visible (VIS, day only) and infrared (IR) atmospheric window radiance measurements. Multi- spectral imagers aboard polar orbiting satellites are the Advanced Very High Resolution
Radiometer (AVHRR, with 5 spectral channels) aboard the National Oceanic and
Atmospheric Administration (NOAA) satellites and the MODerate resolution Imaging
Spectroradiometer (MODIS with 36 spectral channels) aboard the National Aeronautics and
Space Administration (NASA) Earth Observation System (EOS) satellites Terra and Aqua.
Measurements of the same scene under different viewing angles allow a stereoscopic retrieval of cloud top height. Together with the use of polarization the cloud thermodynamic phase can be determined (since non-spherical ice particles polarize the scattered light differently than liquid spherical droplets). The Multi-angle Imaging SpectroRadiometer
(MISR, with 4 solar spectral channels and 9 views) aboard Terra and a sensor using
POLarization and Directionality of the Earth’s Reflectances (POLDER, with 8 solar sub- spectral channels - including 3 polarized - and up to 16 views) aboard PARASOL, being part of the A-Train, both operate during daylight conditions. Results from the Along Track
Scanning Radiometer (ATSR, with 7 channels exploring solar to thermal infrared spectrum and 2 views) aboard the European Space Agency (ESA) platforms ERS-2 and Envisat are also provided only for daylight, but a stereoscopic retrieval has not yet been developed.
IR sounders, originally designed for the retrieval of atmospheric temperature and humidity profiles, use IR channels in absorption bands of CO2, water vapor and ozone.
Measured radiances near the centre of the CO2 absorption band are only sensitive to the upper atmosphere while radiances from the wing of the band arise from successively lower levels in the atmosphere. The operational High resolution Infrared Radiation Sounder (HIRS, with 19
6 channels in the IR) is a multi-channel radiometer, whereas the Atmospheric Infrared Sounder
(AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) are newer infrared spectrometers. Their spatial resolution is about 15 km (at nadir). Several MODIS channels are similar to those of HIRS, allowing for a similar analysis as for HIRS. The variable atmospheric opacity of the many channels measured by these IR sounding instruments allows a more reliable identification of cirrus (semi-transparent ice clouds), day and night. Sounder systems usually include microwave sounders (Microwave Sounding Unit, MSU, and
Advanced Microwave Sounding Unit, AMSU) as well. Because the latter operate at wavelengths insensitive to clouds (sensitive to precipitation, however), they are also used in the retrieval of atmospheric profiles and may be used to improve cloud detection (by predicting IR clear sky radiances).
Solar occultation limb sounders, such as the spectrometer of the Stratospheric Aerosol
Gas Experiment (SAGE) that measures occultation along the Earth’s limb at 4 solar wavelengths, provide good vertical resolution (1 km) at the expense of a low horizontal resolution along the viewing path (only about 200 km). On the other hand, the long atmospheric pathlength permits the detection of subvisible (optically very thin) cirrus (Wang et al. 2001).
Passive microwave imagers, like the Special Sensor Microwave Imager (SSM/I) and the
Advanced Microwave Sounding Radiometer-EOS (AMSR-E), have frequencies that are sensitive to cloud liquid water (and water vapor) as well as scattering by precipitation-sized ice particles. They may be used to estimate cloud liquid water path over ocean, if precipitation and drizzle contamination are removed.
Active sensors extend the measurements of passive radiometers to cloud vertical profiles.
Since 2006 the CALIPSO lidar and CloudSat radar, together, determine cloud top and base heights of all cloud layers (Stephens et al. 2002). Whereas the lidar is highly sensitive and can
7 even detect sub-visible cirrus, its beam only reaches cloud base for clouds with an optical depth less than 3. When the optical depth is larger, the radar is still capable of providing a cloud base location. However, the radar signal needs an optical depth greater than about 1.5 to detect a cloud. Even though the nadir-pointing, active instruments have poor global sampling, the synergy with the passive instruments participating in the A-Train satellite formation
(MODIS, AIRS and POLDER) can be used to better study the vertical structure of different cloud types.
Description of datasets
To resolve the diurnal cycle of clouds the GEWEX cloud climate record, ISCCP, emphases temporal resolution (eight observations per day), rather than spectral resolution. To achieve this goal with uniform global coverage, the only possibility still is to use VIS (day only) and IR atmospheric window radiance measurements from imagers on the suite of geostationary and polar orbiting weather satellites. For a more consistent comparison with the other datasets in the assessment, ISCCP has provided L3 data at four specific local observation times 3:00 AM, 9:00 AM, 3:00 PM and 9:00 PM (the original product is available eight times per day). Cloud pressure (CP) is obtained from the IR radiances and cloud optical depth (COD) is obtained from the VIS radiances, assuming an average effective cloud particle radius (CRE). CRE (and a revised COD, not included here) are retrieved from AVHRR measurements by using near-infrared (NIR, around 4 m) spectral information.
The Pathfinder Atmospheres Extended (PATMOS-x) was developed by NOAA to take full advantage of all five channels of the AVHRR sensor aboard the NOAA and European
Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) polar orbiting platforms. Cloud detection is based on Bayesian classifiers derived from CALIPSO
(Heidinger et al. 2010), and the retrieval is based on the Optimal Estimation Method
(Heidinger and Pavolonis 2009). First CP and cloud emissivity (CEM) are obtained using two
8 IR channels, then COD and CRE are obtained from solar channels so that finally cloud water path (CWP) can be determined from COD and CRE.
The ATSR-GRAPE cloud products (CP, COD, CRE) are retrieved only during day, also using an Optimal Estimation (OE) approach on the five available VIS / NIR / IR channels
(Poulsen et al. 2010). CWP is determined from COD and CRE.
IR Sounder data have been analyzed to obtain CP and CEM by using two approaches: the
‘CO2 slicing’ (HIRS-NOAA, Wylie et al. 1994, 2006), which is used at lower atmospheric pressures up to 650 hPa and which is then complemented by the use of an IR atmospheric
2 window radiance, and a weighted method using the same CO2 absorbing channels (TOVS
Path B and AIRS-LMD, Stubenrauch et al. 1999, 2006, 2010). The latter datasets also include CREI and CIWP for cirrus, the retrieval based on a Look-Up Table (LUT) approach and spectral emissivity differences between 8 and 12 m (Rädel et al. 203, Guignard et al.
2012).
MODIS measurements are transformed into cloud properties by two teams. The MODIS
Science Team (MODIS-ST) uses ‘CO2 slicing’ to determine CP and CEM (Menzel et al.
2008) and a LUT approach on VIS / NIR channels to retrieve COD and CRE (Platnick et al.
2003). The MODIS CERES Science Team (MODIS-CE) uses IR radiances to determine CT and CEM and during the day VIS / NIR radiances together with a LUT approach to retrieve
COD and CRE.
POLDER determines cloud thermodynamical phase (Gouloub et al. 2000) and COD using
VIS / NIR polarization and a LUT approach. CP is determined through differential absorption using 2 channels in the O2 A-band (Ferlay et al. 2010).
MISR provides a stereoscopic cloud top height (CZ) from multi-spectral and multi-angular
VIS / NIR measurements (Di Girolamo et al. 2010).
The active lidar measurements of the CALIPSO mission are also analyzed by two teams:
9 the CALIPSO Science Team (CALIPSO-ST) determines cloud top height from VIS backscatter and identifies cloud ice from depolarization (Winker et al. 2009). Noise is reduced by horizontal averaging. The GCM-Oriented CALIPSO Cloud Products (CALIPSO-
GOCCP) reduce noise by vertical averaging (Chepfer et al. 2010).
Detailed retrieval descriptions may be found in the references of Table 1 and in the
GEWEX Cloud Assessment report (Annex I in Stubenrauch et al. 2012).
CLOUD AMOUNT
Cloud amount (CA), also often referred to as cloud cover, is the ratio between the number of samples that contain clouds and the number of all measurement samples. How instrument resolution (footprint size) affects the estimate of cloud amount has already been studied by
Wielicki and Parker (1992) and Rossow et al. (1993): one would expect an increase in CA by decreasing the spatial resolution (with the same detection sensitivity), especially in the case of low-level clouds which appear to be broken and more variable at smaller scales than upper- level clouds. However, the total cloud amount determined by a particular instrument also depends on the sensitivity of its measurements to the presence of clouds.
Global total cloud amount (Figure 1) is about 0.68 (±0.03) when considering only
clouds with optical depth > 0.1. This value increases to about 0.73 when including sub-
visible cirrus (CALIPSO-ST) and decreases to about 0.56 for clouds with optical depth >
2 (POLDER).
The average global inter-annual variability in CA is about 0.03, about ten times
smaller than the typical day-to-day variability over the globe.
According to most datasets there is about 0.10 to 0.15 more cloudiness over ocean
than over land.
Only HIRS-NOAA and MISR detect a ocean-land difference of 0.30, which can be attributed
10 to lowered sensitivity for cloud detection over land (HIRS misses low-level clouds and MISR misses thin cirrus) and to diurnal sampling bias for MISR, which samples only morning conditions (+0.07: due to slightly larger CA over ocean and significantly smaller CA over land in the morning compared to the afternoon).
The latitudinal variation in CA (Figure 2, upper left panel) of all datasets agrees
well (except for polar regions and HIRS-NOAA in Northern Hemisphere (NH)
midlatitudes), indicating subtropical subsidence regions with about 0.10 and 0.15 less
cloudiness than the global mean at around 20S and 20S respectively and the storm regions
in the Southern Hemisphere (SH) midlatitudes with 0.15 to 0.25 more cloudiness than the
global mean at around 60S.
This behaviour is also shown by the geographical map of regional variations of CA with respect to the global annual mean (0.66), as determined by ISCCP.
Derived cloud amounts depend on instrument capabilities and retrieval performance. To illustrate the spread due to differing sensor sensitivities and retrieval methodologies, Figure 2 presents geographical maps of local differences between maximum and minimum CA value of six datasets (ISCCP, PATMOS-x, MODIS-ST, MODIS-CE, AIRS-LMD and TOVS Path-
B), both in a relative and in an absolute sense. The six datasets have been chosen after eliminating datasets taking data at different observation times (MISR and ATSR-GRAPE) and two outliers (HIRS-NOAA, with low sensitivity to low-level clouds, and POLDER, providing information for clouds with optical depth > 2 (Zeng et al. 2011)). The CALIPSO datasets were eliminated because of their large sampling noise at 1° latitude x 1° longitude. The global spread in CA of these six datasets corresponds to only 0.08 (Figure 1). However, locally, uncertainties in detecting clouds within the datasets may reach 0.4 over deserts and mountains. Another feature is the InterTropical Convergence Zone (ITCZ) where different sensitivities to thin cirrus may lead to a spread of about 0.15 in CA. The subtraction of the
11 global annual means of the considered datasets leads to slightly improved uncertainty patterns in CA, emphasizing the good agreement for latitudinal variation.
Most datasets also agree on the magnitude of the seasonal cycle.
In general, the seasonal variations are smaller than the latitudinal variations, except for the transition of the ITCZ towards the summer hemisphere, which produces a change of about
0.30 over land in the latitude band 0°-30S. Over ocean in the NH midlatitudes the seasonal change is about 0.15, with a minimum of cloudiness in late summer, whereas in the SH midlatitudes, it is negligible.
CLOUD TOP LOCATION
Cloud top location can be retrieved in terms of cloud top temperature (CT), pressure (CP) or height (CZ) above mean sea level. For the conversion among these variables one uses atmospheric temperature profiles, which are either retrieved (e.g. for ISCCP, TOVS Path-B and AIRS-LMD) or adopted from reanalyses (e.g. from National Centers for Environmental
Prediction (NCEP) for PATMOS-x, MODIS-ST and HIRS-NOAA, European Centre for
Medium-Range Weather Forecasts (ECMWF) for ATSR-GRAPE) or taken from weather forecast (e.g. from Global Modeling and Assimilation Office (GMAO) for MODIS-CE and
CALIPSO). Differences in monthly statistics can also arise because of differing detection sensitivity to thin, high clouds.
In general, passive remote sensing provides cloud properties as observed from above.
Therefore high-level clouds correspond to all high-level cloud situations, including single and multiple cloud layers, whereas mid-level and low-level clouds correspond only to situations with no higher altitude clouds above.
Cloud top height (CZ) can be accurately determined with lidar (e.g. CALIPSO).
Apart from the MISR stereoscopic height retrieval for optically thick clouds, passive
12 remote sensing provides a ‘radiometric height’, lying near the middle between cloud top and
‘apparent’ cloud base (for optically thick clouds height at which the cloud reaches an optical depth of 3). It may lie as much as a few kilometers below the ‘physical height’ of the cloud top, depending on the cloud extinction profile and vertical extent (cf. Liao et al. 1995, Wang et al. 1999, Sherwood et al. 2004, Holz et al. 2008, Stubenrauch et al. 2010). High-level clouds in the tropics generally have such ‘diffusive’ cloud tops (meaning that the optical depth increases only slowly from cloud top downwards) for which retrieved cloud temperature may be as much as 10 K larger than cloud top temperature (Figure 3).
Most sensors measuring atmospheric IR window radiances directly retrieve cloud top temperature (CT), when clouds act as blackbody emitters (especially low-level clouds). For semi-transparent clouds the retrieved cloud temperature is biased high because of the atmospheric and surface radiation passing through these clouds and needs to be corrected, in general by using information on the cloud VIS optical depth or IR emissivity. In the case of multiple cloud layers this correction will be underestimated (cf. Jin and Rossow 1997).
Methods involving differential measurements in strong absorption bands (CO2 or O2) determine cloud pressure (CP). Whereas the sounding of the thermal CO2 absorption band leads to a CP corresponding to the radiometric top, the use of the solar O2 absorption band indicates the middle of the cloud (Ferlay et al, 2010).
Probability density functions (PDFs) of CP and CT are computed by dividing the histograms available in the cloud assessment database by the number of cloudy samples. Thus they reflect how the detected clouds are vertically distributed in the atmosphere. The PDFs in
Figure 3 show a bimodal structure, especially in the tropics. This is the reason why average values of CP and CT may be ambiguous and why it is better to use, in addition to averages over all clouds, height-stratified averages (intervals for height stratification by CP are indicated in Figure 3).
13 The decrease of bimodality and spread in CP and CT from tropics towards poles, shown by all datasets except HIRS-NOAA, is essentially linked to the decrease of the tropopause height and a change in the style of atmospheric storm from convective to baroclinic cyclone.
The strong bimodality in the tropics, which is well represented by MODIS-ST, AIRS-LMD,
HIRS-NOAA and PATMOS-x with strong peaks at 950 hPa and between 250 and 150 hPa, also means that the tropics have few mid-level clouds, in agreement with local observations using ground-based radar (Mace and Benson-Troth 2002). CP distributions of POLDER and
ISCCP are flatter, presenting a larger contribution of mid-level clouds.
CALIPSO is the only mission providing accurate height for cloud top, even for optically very thin clouds such as sub-visible cirrus. Therefore the ‘radiative’ cloud height retrieved by passive remote sensing should lie below the CALIPSO cloud height. This applies especially to high-level clouds with diffusive tops, which are frequently found in the tropics. In addition, the amplitude of the peak maximum should be smaller because of missed sub-visible cirrus.
These criteria are fulfilled by most of the datasets. The peak of ISCCP at very low temperature is explained by the fact that the ISCCP retrieval sets the cloud height to just above the tropopause for optically thin cirrus. A very sharp peak of PATMOS-x in the tropics at 215 K / 150 hPa seems to be suspect and can be probably explained by the fact that the
PATMOS-x retrieval had been trained by CALIPSO data. Note that when CALIPSO and
CloudSat observations are combined a more complete view of cloud vertical structure is obtained (Mace et al. 2009).
HEIGHT-STRATIFIED CLOUD AMOUNT
Height-stratified cloud amount relative to total cloud amount gives another indication how the detected clouds are vertically distributed in the atmosphere. It is less influenced by differences in cloud detection sensitivity and should also be more useful for comparison with
14 climate models, which tend to under-represent the optically thinner clouds.
The global average fraction of high-level clouds out of all detected clouds varies from 12% to
55% (CAHR, Figure 1). This spread is essentially explained by instrument performance to detect and/or identify thin cirrus, especially when overlying low-level clouds (about 20% of all cloudy situations according to CALIPSO-ST data): Active lidar measurements, IR sounding along the CO2 absorption band and methods using IR spectral differences are powerful for thin cirrus identification (with descending sensitivity from the former to the latter). Visible information (during daytime) is more important for the detection of low-level clouds. Thus the use of different spectral domains is identified as the main reason for discrepancies in retrieved cloud properties, and these can be understood as cloud scene dependent uncertainties and biases. For cases when thin cirrus is overlying low-level clouds, different retrievals provide different answers: Active lidar and IR methods determine the cloud properties of the thin cirrus (CALIPSO-ST, CALIPSO-GOCCP, HIRS-NOAA, TOVS
Path-B, AIRS-LMD, MODIS-ST, MODIS-CE, PATMOS-x), IR – VIS methods (ISCCP,
ATSR-GRAPE) provide the properties corresponding to a ‘radiative’ mean from both clouds while VIS-only methods emphasize the clouds underneath (MISR, POLDER).
About 40 – 50% of all clouds are high-level clouds. The value decreases to 20%
when considering clouds with optical depth > 2 (MISR).
Outliers are HIRS-NOAA (55%, underestimation of low-level clouds leads to overestimation of fraction of high-level clouds) and POLDER (12%, misidentification of high-level clouds as midlevel clouds, because CP determined by O2 absorption corresponds to a deeper level within the cloud).
Only about 15% (±5%) of all clouds correspond to mid-level clouds with no higher
clouds above (CAMR, Figure 1).
Values of POLDER (43%), ATSR-GRAPE (39%) and ISCCP (27%) for mid-level cloud
15 amounts are biased high, because of misidentification of high-level clouds overlying lower- level clouds.
According to the majority of datasets, about 40% (±3%) of all clouds are single-
layer low-level clouds (CALR, Figure 1).
Outliers are HIRS-NOAA with 26% (only one IR channel is not sufficient to identify all low- level clouds) and MODIS-ST with 53% (due to misidentification of optically thin cirrus, Holz et al. 2008).
By using solar reflectances alone, MISR determines also the height of the low-level cloud when cirrus is present above, leading to a relative low-level cloud amount of about 60%, in agreement with 57% from CALIPSO-GOCCP when not only the uppermost clouds are considered but all cloud layers within the atmosphere. This means that about one third of the coverage of all low-level clouds is overlapped by semi-transparent higher-level clouds (also found by studying the frequency of semi-transparent cirrus overlying clouds at lower levels of
CALIPSO-ST, cf. Jin and Rossow 1997).
Whereas absolute values of height-stratified cloud amount depend on instrument
sensitivity, geographical distributions and latitudinal variations (Figure 2) as well as
seasonal cycles of all datasets show very similar features.
Exceptions are polar regions (CAHR in SH and CALR in NH) and CALR of HIRS-
NOAA. The geographical maps of the difference between maximum and minimum value of the regional variation as well as of the absolute value of CAHR and CALR out of the six chosen participating cloud datasets (as for CA, see above), also presented in Figure 2, show the spread of CAHR and CALR due to different sensor sensitivity and retrieval methodology.
Whereas the global spread in CAHR and CALR of these datasets correspond to about 0.2
(Figure 1), local spreads of CAHR and CALR may reach even 0.4 (ITCZ and deserts).
However, considering variations instead of absolute values (by subtracting global annual
16 means of the considered datasets) leads to spreads mostly less than 0.2 (slightly smaller for
CAHR than for CALR).
RADIATIVE CLOUD PROPERTIES
Cloud emissivity (CEM) is retrieved at thermal wavelengths, and values lie between 0 and
1. Its global average is about 0.7 (varying from 0.6 to 0.8). Effective cloud amount (CAE, cloud amount weighted by cloud emissivity) includes the radiative effect of the detected clouds. Its global average is about 0.50.
The global effective amount of high-level clouds (0.15) agrees much better between
the different datasets than CAHR, because a smaller cloud amount due to missing thin
clouds is compensated by a larger average cloud emissivity (Figure 1).
CLOUD OPTICAL AND BULK MICROPHYSICAL PROPERTIES
Since cloud liquid droplets and ice crystals have different optical properties (linked to refractive index, particle shape and size), it is necessary to distinguish the cloud thermodynamical phase before retrieving cloud optical depth and bulk microphysical properties. Liquid and ice clouds are distinguished by polarization measurements (POLDER,
CALIPSO), by cloud temperature (ISCCP: ice clouds CT < 260 K, AIRS-LMD, TOVS Path-
B: pure ice clouds CT < 230 K, excluding mixed phase clouds) or by use of multi-spectral information (PATMOS-x, MODIS and ATSR-GRAPE). As shown in Figure 4, the global average fraction of ice clouds relative to all clouds (CAIR) lies between 20% (corresponding to pure ice clouds colder than 230 K, without considering mixed phase clouds, thus likely an underestimate) and 70% (lidar backscatter depolarization), with values around 35% when spectral variation methods are used. Average cloud temperature of definite ice clouds (colder than 230 K) is about 220 K. When warmer ice clouds and possibly mixed-phase clouds are
17 included in the ice cloud category (all datasets except TOVS Path-B and AIRS-LMD), the average ice cloud temperature is about 250 K (Figure 4).
Cloud optical depth (COD) is usually retrieved from non-absorbing solar reflectances (0.5
– 0.9 m) and therefore only available during daytime, but higher time resolution results from geostationary observations do suggest systematic diurnal variations (Rossow and Schiffer
1999). Given the strong non-linear relationship between reflectance and COD, the most precise COD values lie between 2 and 50. Whereas cloud water path (CWP) strongly influences COD and CEM, cloud effective particle radius (CRE, averaged over a size distribution within the cloud) can be obtained due to spectral dependency in absorption and scattering in the solar or thermal domain, especially when particles are smaller. At constant
CWP, decreasing CRE makes the solar albedo increase. Optical methods determine CRE for all clouds. However, in the case of optically thick clouds CRE only relates to the upper part of the cloud. This may introduce CRE biases (typically, overestimates for liquid clouds and underestimates for ice clouds). Other sources of uncertainty are assumed particle shape and size distribution within the cloud. Height contributions of CRE depend on the absorbing spectral band used in the retrieval: in general, absorption increases with increasing wavelength (Platnick et al. 2003). Therefore IR Sounders provide estimates of CRE only for semi-transparent cirrus. CWP can be estimated from COD if CRE is known. Whereas the standard ISCCP product assumes values for CRE in its retrieval of COD (the values of CRE included here for ISCCP come from a special analysis of AVHRR data), other methods retrieve CRE and COD together, the latter method providing a better estimate.
Global COD varies between 4 and 10 (Figure 4). For comparison, CEM determined by IR sounders was converted to COD which is then limited to values 10, leading to smaller COD average CODs. Retrieval sub-sampling by MODIS-ST (optical and microphysical properties are only reported for clouds with 100 > COD > 1) and by ATSR-GRAPE (OE retrieval
18 method successful only for about 40% of all clouds, with a bias towards optically thick clouds) leads to larger averages for these products (Figure 5). Given a global mean cloud amount of nearly 0.70, the radiative mean cloud COD has to be < 4 to give a planetary albedo near 0.3.
Since PDFs of COD are not Gaussian (Figure 5) and averages depend on sub-sampling prior to retrieval, it is strongly recommended to consider the distributions instead of averages. One can distinguish three groups in Figure 5: clouds with COD < 1, with COD between 1 and 10 and with COD > 10. The main contribution to global averages comes from clouds with COD between 1 and 10 (except ATSR-GRAPE), and the relative contributions outside this range essentially reflect differences in data selection for the retrieval.
Global effective particle radii are about 14 m (±1 m) and 25 m (±2 m), for the
tops of liquid clouds and for high-level ice clouds respectively (Figure 4).
Effective cloud droplet radii (CREW) are on average about 15 – 20% larger over
ocean than over continents, whereas the difference in effective ice crystal radius (CREI) is
only about 5%.
All PDFs of effective cloud droplet radius (CREW) show a large peak around 11 m.
Additional smaller peaks around 2 m (ISCCP and PATMOS-x) and 40 m (ISCCP) can be explained by partly cloudy samples and by thermodynamical phase misidentification, respectively.
Assumptions on ice crystal shape lead to additional uncertainties in retrieved effective ice crystal radius (Zhang et al, 2009; Zeng et al, 2012): ISCCP, TOVS Path-B and ATSR-
GRAPE assume ice crystal aggregates, MODIS-ST uses a mixture of ice crystal shapes and
AIRS-LMD estimates the most probable shape between ice crystal aggregates and pristine hexagonal columns, the fraction of aggregates increasing with CIWP (Guignard et al. 2012).
The PDFs of CREI (Figure 5) fall into two categories: those using the spectral absorption at
19 IR (8.7 m, TOVS Path-B and AIRS-LMD) or NIR (3.7 m, ISCCP, PATMOS-x and
MODIS-CE) and those using SWIR (2.1 m, MODIS-ST, or 1.6 m, ATSR-GRAPE) wavelengths. PDFs of the first category exhibit a large peak around 32 m with a plateau down to 20 m, whereas PDFs of the second category exhibit a peak around 27 m. Spectral absorption increases slightly with wavelength, so that by using shorter wavelengths one would expect to retrieve a CREI slightly deeper inside the cloud, leading to larger CREI (ice crystal size increases from cloud top to base due to aggregation processes), when the cloud statistics are similar. Therefore, smaller peak values of CREI retrieved by MODIS-ST and ATSR-
GRAPE may again be explained by sub-sampling, because CREI is retrieved closer to the cloud top. A smaller peak at CREI of around 18 m produced by ISCCP can be probably explained by misidentified liquid clouds (or mixed phase clouds).
Global cloud water path varies from 30 to 60 gm-2 for liquid clouds and from 60 to
120 gm-2 for clouds with ice tops (Figure 4). Note that these values for ice clouds include
all of the cloud water in the column, some of which may actually be liquid (cf. Lin and
Rossow 1996, Lin et al. 1998).
Sub-sampling of ice clouds leads to smaller (25 gm-2 for semi-transparent cirrus
from AIRS-LMD) or larger values (225 gm-2 for clouds with optical depth larger than 1
from MODIS-ST).
PDFs of CLWP of all datasets have a peak around 70 gm-2. A second peak around smaller values (1.5 gm-2 for PATMOS-x and ATSR-GRAPE and 8 gm-2 for ISCCP) may partly stem from partly cloudy samples or cloud edges.
PDFs of CIWP depend strongly on retrieval sub-sampling, with largest peaks around 5 gm-2 from datasets with no sub-sampling (ISCCP and PATMOS-x). Peaks move to 10 gm -2 and 30 gm-2 when excluding clouds with CEM < 0.2 (COD < 0.45) and CEM < 0.3 (COD < 0.7)
20 respectively (AIRS-LMD and TOVS Path-B). The peak value is at to 70 gm-2 when excluding clouds with COD < 1 (MODIS-ST).
The latitudinal variation of the retrieved cloud bulk microphysical properties is
essentially expressed by the relative height of the peaks at small and larger values. This
means that the variation (especially of CIWP) is directly linked to the difference in
occurrence of optically thin and thick clouds included in each product.
Seasonal variations …..
DIURNAL VARIATIONS
Based on ISCCP results (Cairns 1995, Rossow and Cairns 1995, Rossow and Schiffer
1999), the most noticeable features of the diurnal cycle of clouds are significant differences between the phase of low-level variations over ocean (morning maximum) and land
(afternoon maximum) and between the phase of low-level and high-level cloud variations (the latter have a maximum early to late evening). These findings are complemented by analyses of IR sounder observations (exploiting the drifting NOAA satellites, Stubenrauch et al. 2006), which demonstrate that cirrus increase during the afternoon and gradually thicken into the nighttime. The GEWEX Cloud Assessment was mainly focused on monthly averages and longer-term variations. However, diurnal variations can affect these results. Day-night differences and daytime sampling differences among datasets with no change in method (IR sounders and lidar) reflect random differences of a few percent (section 3.1.3 in Stubenrauch et al. 2012). CALIPSO seems to have a slightly smaller detection sensitivity for optically thin cirrus during the day (5 to 10% in CAHR over tropical land), linked to solar radiance noise.
Day-night differences for ISCCP correspond to 5-10% in CA over land (corrected by temporal interpolation in the official ISCCP version) and approach 25% in CAHR in the tropics, the latter due primarily to the inability to adjust the height for transmissive clouds without COD information (both of these effects corrected for in the official ISCCP product).
21 LONGTERM VARIATIONS
Interannual variability indicates natural noise which should be considered when analyzing trends. Global interannual variability lies between 0.02-0.03 in cloud amount, 2.5-3.5% in relative high-level / low-level cloud amount and around 2 K in cloud temperature. Natural interannual variability increases when considering specific regions: The most prominent feature in regional interannual variability is associated with El Niño Southern Oscillation.
Monitoring longterm variations with these datasets requires consideration of many factors.
Due to systematic variations of cloud properties with geographical location, time of day and season, any systematic variations in sampling of these distributions can introduce trend artefacts in the long-term record. These have to be carefully investigated before attributing any detected trends to climate change, which has not yet been done for any of the cloud products considered here.
CONCLUSIONS, RECOMMENDATIONS AND OUTLOOK
The GEWEX Cloud Assessment database, created by the participating teams, allowed for the first time an inter-comparison of L3 cloud products of twelve global ‘state of the art’ datasets. In addition to self-assessments (Annex I of Stubenrauch et al. 2012) which show the maturity of the various datasets, the analyses have shown how cloud properties are perceived by instruments measuring different parts of the electromagnetic spectrum and how cloud property averages and distributions are affected by instrument choice as well as some methodological decisions. These satellite cloud products are very valuable for climate studies or model evaluation: Even if absolute values, especially those of high-level cloud statistics depend on instrument (or retrieval) performance to detect and/or identify thin cirrus, relative geographical and seasonal variations in the cloud properties agree very well (with only a few exceptions like deserts and snow-covered regions). Probability density functions of optical
22 and bulk microphysical properties also agree well, when one considers retrieval sub-sampling or possible biases due to partly cloudy samples and to ice-water misidentification.
So far only ISCCP cloud properties have been tested by comparing resulting radiative fluxes to those determined from Earth Radiation Budget instruments, revealing excellent quantitative agreement (Zhang et al. 2004, GEWEX Assessment of Global Radiative Flux
Datasets, Raschke et al. 2012). At present the ISCCP data record is being reprocessed. This kind of assessment should be regularly repeated, in a cycle of eight to ten years. The current
GEWEX Cloud Assessment database will facilitate future activities but can be used for model evaluations, since the multiple products can used as a cross-check on the observations.
However, for a better effectiveness, especially detailed investigation of the differences, future assessments should be supported by funding. EUMETSAT has initiated the Cloud Retrieval
Evaluation Workshop (CREW, http://www.icare.univ-lille1.fr/crew/index.php/Welcome) focusing on detailed L2 data comparisons over limited areas and time periods, and ESA included assessments of the Essential Climate Variables retrieved within the Climate Change
Initiative.
ACKNOWLEDGMENTS
B. Baum, C. G. Campbell (workshop chairs), Y. Chen, A. Feofilov, A. Menzie, E. Olson, F.
Parol, D. Wylie and especially other product team members)
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28 FIGURE CAPTIONS
Figure 1 : Global averages of total cloud amount (CA) and of high-level, mid-level and low- level cloud amount relative to total cloud amount (CAHR + CAMR + CALR = 1). Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except MISR and ATSR-
GRAPE at 10:30 AM LT).
Figure 2 : from left to right: Latitudinal variation relative to global annual mean of all cloud datasets, geographical map of variation relative to global annual mean of ISCCP, as well as geographical maps of the spread between maximum and minimum within six cloud datasets
(ISCCP, PATMOS-x, MODIS-ST, MODIS-CE, AIRS-LMD and TOVS Path-B) of the regional variation and of the absolute value of total cloud amount (CA, top), relative high- level cloud amount (CAHR, middle) and low-level cloud amount (CALR, bottom). Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except MISR and ATSR-
GRAPE at 10:30 AM LT in the left panel).
Figure 3 : Normalized frequency distributions of cloud temperature (CT, upper panel) and of cloud pressure (CP, lower panel) in tropics (15N-15S), midlatitudes (30°-60°) and polar latitudes (60°-90°). Statistics for 2007 daytime measurements (1:30 – 3:00 PM LT).Interval limits for the definition of high-level, mid-level and low-level clouds are indicated as broken lines at 440 hPa and 680 hPa (corresponding to altitudes of about 6 km and 3 km, respectively).
Figure 4 : Global averages of cloud properties of ice clouds (I, left) and of liquid clouds (W, right): relative amount (CAR), temperature (CT), IR effective emissivity (CEM), VIS optical depth (COD), water path (CWP) and effective radius (CRE). CAWR + CAIR = 100%, except
AIRS-LMD and TOVS Path-B for which the missing 35% correspond to clouds of mixed phase (230 K < CT < 260 K). CODI, CIWP and CREI are given for high-level ice clouds
29 instead of all ice clouds (except PATMOS-x and MODIS-ST). Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except ATSR-GRAPE at 10:30 AM LT).
Figure 5: Normalized frequency distributions of cloud properties of ice clouds (I, left) and of liquid clouds (W, right): optical depth (COD), water path (CWP) and effective radius (CRE).
Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except ATSR-
GRAPE at 10:30 AM LT).
Figure 6: Seasonal cycle of cloud properties, separately for ice clouds (left) and for liquid clouds (right) in NH midlatitudes (30N-60N) and in SH midlatitudes (30S-60S): relative fraction of clouds, optical depth, water path and effective particle radius. Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT).
30 Table 1 Participating Datasets, type of sensors, local observation times and time period in the database ISCCP multi-spectral imagers 3:00, 9:00 AM/PM 1983-2007 (Rossow and Schiffer 1999) AVHRR Pathfinder PATMOS-x multi-spectral imagers 1:30, 7:30 AM/PM 1982-2009 () MODIS Science Team multi-spectral imagers 1:30, 10:30 AM/PM 2001-2009 () MODIS CERES Science Team multi-spectral imagers 1:30, 10 :30 AM/PM 2003-2008 () HIRS-NOAA IR sounders 1:30, 7:30 AM/PM 1987-2006 () TOVS Path-B IR sounders 1:30, 7:30 AM/PM 1987-1994 (Stubenrauch et al. 2006, Rädel et al. 2003) AIRS-LMD IR sounder 1:30 AM/PM 2003-2009 (Stubenrauch et al. 2010, Guignard et al. 2012) CALIPSO Science Team lidar 1:30 AM/PM 2007-2008 () CALIPSO-GOCCP lidar 1:30 AM/PM 2007-2008 (Chepfer et al. 2010) POLDER multi-angle imager 1:30 PM 2006-2008 (Parol et al. 2004) MISR multi-angle imager 10:30 AM 2001-2009 () ATSR-GRAPE multi-angle imagers 10:30 AM 2003-2009 ()
31 Table 2 Cloud Properties in GEWEX Cloud Assessment database and their range: Cloud amount (fractional cloud cover) CA (0-1) Cloud temperature at top CT (150-340 K) Cloud pressure at top CP (1013-100 hPa) Cloud height (above sea level) CZ (0-20 km) Cloud IR emissivity CEM (0-1) Effective Cloud amount (CA weighted by CEM) CAE (0-1) Cloud (visible) optical depth COD (0-400) Cloud water path (liquid, ice) CLWP, CIWP (0-3000 g/m2) Cloud effective particle size (liquid, ice) CREW, CREI (0-200 m)
Statistics of these variables are provided for all clouds and separately stratified by cloud top height category, defined by cloud top pressures as in ISCCP (high-level with CP < 440 hPa, mid-level with 440 hPa < CP < 680 hPa and low-level with CP > 680 hPa), and by cloud thermodynamical phase (liquid, ice), distinguished by CT (ISCCP, TOVS Path-B, AIRS-LMD), by spectral radiance differences (PATMOS-x, MODIS, ATSR-GRAPE) or by polarization signature (POLDER, CALIPSO).
32 33 Figures
Figure 1 : Left: Global averages of total cloud amount (CA) and of high-level, mid-level and low-level cloud amount relative to total cloud amount (CAHR + CAMR + CALR = 1). Right:
Global averages of effective cloud amount (cloud amount weighted by IR cloud emissivity) of high-level clouds (CAEH), of mid-level clouds (CAEM) and of low-level clouds (CAEL).
Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except MISR and
ATSR-GRAPE at 10:30 AM LT).
34 CA - CA -
CAHR - CAHR -
CALR - CALR -
-0.25 -0.05 0.15 0.35 0.1 0.3 0.5 0.7 0.1 0.3 0.5 0.7
Figure 2 : from left to right: Latitudinal variation relative to global annual mean of all cloud datasets, geographical map of variation relative to global annual mean of ISCCP, as well as geographical maps of the spread between maximum and minimum within six cloud datasets
(ISCCP, PATMOS-x, MODIS-ST, MODIS-CE, AIRS-LMD and TOVS Path-B) of the regional variation and of the absolute value of total cloud amount (CA, top), relative high- level cloud amount (CAHR, middle) and low-level cloud amount (CALR, bottom). Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except MISR and ATSR-
GRAPE at 10:30 AM LT in the left panel).
35 Figure 3 : Normalized frequency distributions of cloud temperature (CT, upper panel) and of cloud pressure (CP, lower panel) in tropics (15N-15S), midlatitudes (30°-60°) and polar latitudes (60°-90°). Statistics for 2007 daytime measurements (1:30 – 3:00 PM LT).Interval limits for the definition of high-level, mid-level and low-level clouds are indicated as broken lines at 440 hPa and 680 hPa (corresponding to altitudes of about 6 km and 3 km, respectively).
36 Figure 4 : Global averages of cloud properties of ice clouds (I, left) and of liquid clouds (W, right): relative amount (CAR), temperature (CT), IR effective emissivity (CEM), VIS optical depth (COD), water path (CWP) and effective radius (CRE). CAWR + CAIR = 100%, except
AIRS-LMD and TOVS Path-B for which the missing 35% correspond to clouds of mixed phase (230 K < CT < 260 K). CODI, CIWP and CREI are given for high-level ice clouds instead of all ice clouds (except PATMOS-x and MODIS-ST). Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except ATSR-GRAPE at 10:30 AM LT).
37 Figure 5 : Normalized frequency distributions of cloud properties of ice clouds (I, left) and of liquid clouds (W, right): optical depth (COD), water path (CWP) and effective radius (CRE).
Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT, except ATSR-
GRAPE at 10:30 AM LT).
38 Seasonal cycle of ice clouds and of liquid clouds
Figure 6: Seasonal cycle of cloud properties, separately for ice clouds (left) and for liquid clouds (right) in NH midlatitudes (30N-60N) and in SH midlatitudes (30S-60S): relative fraction of clouds, optical depth, water path and effective particle radius. Statistics are averaged over daytime measurements (1:30 – 3:00 PM LT).
39